import pandas as pd
import numpy as np
import os
os.chdir("/home/shahul/Downloads/Data_Set_Qualifiers/")
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
plt.style.use('ggplot')
Edta of each mode of transport
path="Mobility/Kochi Metro/KMRL - CSV/"
fares=pd.read_csv(path+"/Fares.csv", encoding = "ISO-8859-1")
routes=pd.read_csv(path+"/Routes.csv", encoding = "ISO-8859-1")
schedules=pd.read_csv(path+"/Schedules.csv", encoding = "ISO-8859-1")
stops=pd.read_csv(path+"/Stops.csv", encoding = "ISO-8859-1")
fares.shape
fares.describe()['Aluva']
plt.figure(figsize=(9,7))
fareper=(fares.iloc[:,:2]['Aluva']/schedules['dist_traveled (km)'][0:16]).replace(np.inf,0)
plt.plot(np.arange(0,16),fareper)
plt.xlabel("No of stops")
plt.ylabel("Fare per stop")
plt.savefig("fares.png")
plt.show()
routes.head()
schedules.shape
schedules.info()
schedules['arrival_time']=schedules['arrival_time'].apply(lambda x:datetime.strptime(x,"%H:%M:%S"))
schedules['departure_time']=schedules['departure_time'].apply(lambda x:datetime.strptime(x,"%H:%M:%S"))
schedules['delta']=(schedules['departure_time']-schedules['arrival_time']).apply(lambda x:x.seconds)
stop=schedules.groupby("stop_id",as_index=False)['delta'].agg('mean')
plt.figure(figsize=(7,7))
sns.barplot(y=stop['stop_id'],x=stop['delta'])
plt.xlabel('Time in seconds')
plt.title("Average waiting time in each stops")
plt.savefig("avg_wait.png")
Mode=["Ferry","Ferry Vega","Bus","car","walk","Bicycle","metro"]
speed=[14,22,20,28,5,16,35]
co2=[13,20,18,135,0,0,10]
extra_df=pd.DataFrame({"Mode":Mode,"speed":speed,"co2":co2})
extra_df.head()
plt.figure(figsize=(8,5))
sns.barplot(y=extra_df['Mode'],x=extra_df['speed'])
plt.xlabel("speed in kmph")
plt.title(' Speed by each transport')
plt.show()
plt.savefig('speed.png')
plt.figure(figsize=(8,5))
sns.barplot(y=extra_df['Mode'],x=extra_df['co2'])
plt.xlabel("CO2 Emission (gm/km/passenger)")
plt.title('Co2 emission by each transport')
plt.show()
plt.savefig('co2.png')
import plotly
import plotly.graph_objs as go
plotly.offline.init_notebook_mode(connected=False)
mapbox_access_token = 'pk.eyJ1Ijoic2hhaHVsZXM3ODYiLCJhIjoiY2pzaWo1dDh0MTZwNjQ0cWdoa2EwYzVvbCJ9.pIF5KdImGJppU17asYDYSg'
data = [
go.Scattermapbox(
lat=stops['stop_lat'],
lon=stops['stop_lon'],
mode='markers',
marker=dict(
size=7
),
text=stops['stop_name'],
)
]
layout = go.Layout(title="metro stations locations",
autosize=True,
hovermode='closest',
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,
center=dict(
lat=10.03,
lon=76.30
),
pitch=0,
zoom=10
),
)
fig = dict(data=data, layout=layout)
plotly.offline.iplot(fig, filename='~/file1.html')
path="Mobility/Ferry/Ferry - CSV/"
f_routes=pd.read_csv(path+"Routes.csv").copy()
f_schedules=pd.read_csv(path+"Schedules.csv").copy()
f_stops=pd.read_csv(path+"Stop_Locations.csv").copy()
f_trips=pd.read_csv(path+"Trips.csv").copy()
f_routes.shape
f_routes.head(5)
len(f_routes['Route Name'].unique())
f_schedules.head()
f_stops.head()
import plotly
import plotly.graph_objs as go
plotly.offline.init_notebook_mode(connected=False)
mapbox_access_token = 'pk.eyJ1Ijoic2hhaHVsZXM3ODYiLCJhIjoiY2pzaWo1dDh0MTZwNjQ0cWdoa2EwYzVvbCJ9.pIF5KdImGJppU17asYDYSg'
data = [
go.Scattermapbox(
lat=f_stops['stop_lat'],
lon=f_stops['stop_lon'],
mode='markers',
marker=dict(
size=7
),
text=f_stops['stop_name'],
)
]
layout = go.Layout(title="Ferry stop locations",
autosize=True,
hovermode='closest',
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,
center=dict(
lat=9.99,
lon=76.35
),
pitch=0,
zoom=10
),
)
fig = dict(data=data, layout=layout)
plotly.offline.iplot(fig, filename='Montreal Mapbox')
f_trips.head()
path="Mobility/Auto Stands Locations/"
auto=pd.read_csv(path+"Auto_Stands.csv")
plotly.offline.init_notebook_mode(connected=False)
mapbox_access_token = 'pk.eyJ1Ijoic2hhaHVsZXM3ODYiLCJhIjoiY2pzaWo1dDh0MTZwNjQ0cWdoa2EwYzVvbCJ9.pIF5KdImGJppU17asYDYSg'
data = [
go.Scattermapbox(
lat=auto['Latitude'],
lon=auto['Longitude'],
mode='markers',
marker=dict(
size=7
),
text=auto['Name'],
)
]
layout = go.Layout(title="Auto stand locations around kochi",
autosize=True,
hovermode='closest',
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,
center=dict(
lat=10.10,
lon=76.35
),
pitch=0,
zoom=10
),
)
fig = dict(data=data, layout=layout)
plotly.offline.iplot(fig, filename='Montreal Mapbox')
path="Non-Mobility/"
accommodation=pd.read_csv(path+"Accommodation.csv")
cultural=pd.read_csv(path+"Cultural.csv")
Gastronomy=pd.read_csv(path+"Gastronomy.csv",encoding= "ISO-8859-1")
Recreation=pd.read_csv(path+"Recreation.csv")
religious=pd.read_csv(path+"Religious_Establishments.csv",encoding= "ISO-8859-1")
accommodation["Category"].unique()
mapbox_access_token = 'pk.eyJ1Ijoic2hhaHVsZXM3ODYiLCJhIjoiY2pzaWo1dDh0MTZwNjQ0cWdoa2EwYzVvbCJ9.pIF5KdImGJppU17asYDYSg'
data = [
go.Scattermapbox(
lat=accommodation[accommodation['Category']=="hostel"]['Latitude '],
lon=accommodation[accommodation['Category']=="hostel"]['Longitude'],
mode='markers',
marker=dict(
size=7
),
text=accommodation[accommodation['Category']=="hostel"]['Name'],
)
]
layout = go.Layout(title="Hostel locations around kochi",
autosize=True,
hovermode='closest',
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,style="streets",
center=dict(
lat=9.96,
lon=76.25
),
pitch=0,
zoom=12
),
)
fig = dict(data=data, layout=layout)
plotly.offline.iplot(fig, filename='Montreal Mapbox')
mapbox_access_token = 'pk.eyJ1Ijoic2hhaHVsZXM3ODYiLCJhIjoiY2pzaWo1dDh0MTZwNjQ0cWdoa2EwYzVvbCJ9.pIF5KdImGJppU17asYDYSg'
data = [
go.Scattermapbox(
lat=accommodation[accommodation['Category']=="hotel"]['Latitude '],
lon=accommodation[accommodation['Category']=="hotel"]['Longitude'],
mode='markers',
marker=dict(
size=7
),
text=accommodation[accommodation['Category']=="hotel"]['Name'],
)
]
layout = go.Layout(title="Hotel locations around kochi",
autosize=True,
hovermode='closest',
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,style="streets",
center=dict(
lat=9.96,
lon=76.25
),
pitch=0,
zoom=9
),
)
fig = dict(data=data, layout=layout)
plotly.offline.iplot(fig, filename='Montreal Mapbox')
mapbox_access_token = 'pk.eyJ1Ijoic2hhaHVsZXM3ODYiLCJhIjoiY2pzaWo1dDh0MTZwNjQ0cWdoa2EwYzVvbCJ9.pIF5KdImGJppU17asYDYSg'
data = [
go.Scattermapbox(
lat=accommodation[accommodation['Category']=="motel"]['Latitude '],
lon=accommodation[accommodation['Category']=="motel"]['Longitude'],
mode='markers',
marker=dict(
size=7
),
text=accommodation[accommodation['Category']=="motel"]['Name'],
)
]
layout = go.Layout(title="motel locations around kochi",
autosize=True,
hovermode='closest',
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,style="streets",
center=dict(
lat=9.96,
lon=76.25
),
pitch=0,
zoom=10
),
)
fig = dict(data=data, layout=layout)
plotly.offline.iplot(fig, filename='Montreal Mapbox')
cat=cultural['Category'].unique()
cultural.head()
def funct(cat):
data=[]
for i in cat:
trace=go.Scattermapbox(
lat=cultural[cultural['Category']==i]['Latitude '],
lon=cultural[cultural['Category']==i]['Longitude'],
mode='markers',
marker=dict(
size=7
),
text=cultural[cultural['Category']==i]['Name'],name=i
)
data.append(trace)
return data
data=[]
data=funct(cat)
layout = go.Layout(title="cultural activities locations around kochi",
autosize=True,
hovermode='closest',
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,style="streets",
center=dict(
lat=9.96,
lon=76.25
),
pitch=0,
zoom=10
),
)
fig = dict(data=data, layout=layout)
plotly.offline.iplot(fig, filename='Montreal Mapbox')
cat=Gastronomy["Category"].unique()
def funct(cat):
data=[]
for i in cat:
trace=go.Scattermapbox(
lat=Gastronomy[Gastronomy['Category']==i]['Latitude'],
lon=Gastronomy[Gastronomy['Category']==i]['Longitude'],
mode='markers',
marker=dict(
size=7
),
text=Gastronomy[Gastronomy['Category']==i]['Name'],name=i
)
data.append(trace)
return data
data=[]
data=funct(cat)
layout = go.Layout(title="Gastronomy locations around kochi",
autosize=True,
hovermode='closest',
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,style="streets",
center=dict(
lat=9.96,
lon=76.25
),
pitch=0,
zoom=10
),
)
fig = dict(data=data, layout=layout)
plotly.offline.iplot(fig, filename='Montreal Mapbox')
cat=Recreation['Category'].unique()
def funct(cat):
data=[]
for i in cat:
trace=go.Scattermapbox(
lat=Recreation[Recreation['Category']==i]['Latitude'],
lon=Recreation[Recreation['Category']==i]['Longitude'],
mode='markers',
marker=dict(
size=7
),
text=Recreation[Recreation['Category']==i]['Name'],name=i
)
data.append(trace)
return data
data=[]
data=funct(cat)
layout = go.Layout(title="Recreation activities locations around kochi",
autosize=True,
hovermode='closest',
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,style="streets",
center=dict(
lat=9.96,
lon=76.25
),
pitch=0,
zoom=10
),
)
fig = dict(data=data, layout=layout)
plotly.offline.iplot(fig, filename='Montreal Mapbox')
cat=religious['Category'].unique()
def funct(cat):
data=[]
for i in cat:
trace=go.Scattermapbox(
lat=religious[religious['Category']==i]['Latitude'],
lon=religious[religious['Category']==i]['Longitude'],
mode='markers',
marker=dict(
size=7
),
text=religious[religious['Category']==i]['Name'],name=i
)
data.append(trace)
return data
data=[]
data=funct(cat)
layout = go.Layout(title="Religious centre locations around kochi",
autosize=True,
hovermode='closest',
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,style="streets",
center=dict(
lat=9.96,
lon=76.25
),
pitch=0,
zoom=10
),
)
fig = dict(data=data, layout=layout)
plotly.offline.iplot(fig, filename='Montreal Mapbox')